Yihao Liu serves as Research Assistant Professor in Vanderbilt University's School of Engineering, Department of Electrical and Computer Engineering, focusing on clinically relevant image analysis for personalized treatment strategies and large-scale medical data interpretation. His work bridges engineering and clinical applications through advanced computational methodologies. Education: PhD in Electrical and Computer Engineering, Johns Hopkins University (May 2024) Dr. Liu's research spans medical image analysis, deformable registration, deep learning, and computer vision with applications in CT, MRI, and OCT imaging. He develops AI-driven tools for pulmonary nodule diagnosis, MS lesion analysis, body composition assessment, and dermatological imaging, emphasizing clinical translation and multi-modal data fusion. His 2024-2025 publications reveal concentrated innovation in diffusion models for field-of-view extension, bi-directional lesion synthesis, and unsupervised registration techniques, demonstrating leadership in foundational registration models and longitudinal analysis for precision medicine. Dr. Liu directs the Vanderbilt Lab for Immersive AI Translation (VALIANT) and collaborates with the Medical-image Analysis and Statistical Interpretation Lab (MASI) under VISE Affiliate Bennett Landman, PhD. He holds the Stevenson Chair in Electrical and Computer Engineering and participates in NIH grant writing initiatives through Vanderbilt's medical partnerships.
Dr. Yi Pan is a Distinguished University Professor and Chair of the Department of Computer Science at Georgia State University. He holds a B.Eng. and M.Eng. from Tsinghua University (China) and a Ph.D. from the University of Pittsburgh (USA). His research focuses on parallel computing, wireless networks, bioinformatics, and machine learning, with over 330 publications and 39 authored/edited books. Dr. Pan has organized international conferences and delivered over 40 keynote speeches globally. Education: B.Eng., Computer Engineering, Tsinghua University, 1982 M.Eng., Computer Engineering, Tsinghua University, 1984 Ph.D., Computer Science, University of Pittsburgh, 1991 Research Interests: Dr. Pan’s work spans parallel and cloud computing, wireless networks, and bioinformatics. His contributions include scalable algorithms for functional module discovery in protein networks, self-defense mechanisms for cyber threats, and efficient routing protocols for sensor networks. Recent efforts integrate AI into healthcare, such as lesion localization in OCT imaging and autism spectrum disorder diagnosis via EEG analysis. Awards & Recognition: IEEE Distinguished Speaker (2000–2002) Yamacraw Distinguished Speaker (2002) Listed in Who’s Who in America and Who’s Who in Computational Science Awards from NSF, AFOSR, IBM, and Mellon Foundation Advising & Leadership: As department chair, Dr. Pan oversees interdisciplinary collaborations between Computer Science and Computer Information Systems. He has advised numerous students and holds editorial roles in 15 journals, including several IEEE Transactions. His grants focus on advancing AI-driven healthcare, cybersecurity, and computational biology. Labs & Teams: Leads research initiatives in bioinformatics, medical AI, and parallel computing, collaborating with industry partners like IBM and Shell Oil. Active in organizing conferences such as the IEEE International Conference on Bioinformatics and Biomedicine.
Daifeng Wang is an Associate Professor at the University of Wisconsin-Madison, holding an affiliate appointment in the Department of Computer Sciences within the College of Letters and Science. His research is conducted through the Waisman Center, where he directs the Daifeng Wang Laboratory focused on developing computational approaches to understand brain function and disease. Dr. Wang's research interests center on developing machine learning approaches and bioinformatics tools to analyze multimodal data for improving genotype-phenotype prediction and understanding functional genomics and gene regulation in human brains and brain diseases. His current research topics include bio-inspired machine learning, single-cell functional genomics, and multimodal integration and imputation. His work bridges computational biology with neuroscience to address complex questions in brain development and disease. Analysis of Dr. Wang's recent publications reveals a strong focus on developing computational frameworks for integrating diverse biological data types. His research demonstrates expertise in applying machine learning to neurogenomics, with particular emphasis on Alzheimer's disease, intellectual and developmental disabilities, and brain organoid models. His work consistently combines theoretical innovation in machine learning with practical applications to pressing biological questions. National Institutes of Health National Science Foundation Simons Foundation Autism Research Initiative University of Wisconsin-Madison Dr. Wang's laboratory develops machine learning and artificial intelligence approaches and bioinformatics tools to bridge computation and biology for mechanistic insights into complex brains and brain diseases. Their applications focus on functional genomics, gene regulation, cell dynamics, and neural circuits, with particular attention to translating computational findings into biological understanding.
Andrew Beharry is an Associate Professor in the Department of Chemical & Physical Sciences at the University of Toronto Mississauga. His research focuses on developing small molecule probes for cancer biology, including fluorescent chemosensors for drug resistance assessment and activatable photosensitizers for photodynamic therapy (PDT). His lab integrates chemical synthesis, biochemical assays, and in vivo models to create diagnostic tools and treatment platforms that address limitations of conventional cancer therapies. Education & Training: B.Sc. (Honours) in Chemistry and Biology, York University (2006) Ph.D. in Chemistry, University of Toronto (2012) Postdoctoral Fellow, Stanford University (2013–2016), funded by the Human Frontier Science Program Research Interests: Beharry’s lab bridges chemistry and biology to tackle cancer challenges. Key areas include: Fluorescent Chemosensors: Rapid, accurate assays for anticancer drug resistance mechanisms (e.g., DNA repair enzymes, cell death pathways). Activatable Photosensitizers: Cancer-selective PDT agents that enable real-time imaging and therapy via biomarker activation. Fluorescence-Guided Therapies: Combining PDT with surgical imaging to reduce cancer recurrence. Achievements & Funding: Recipient of NSERC Discovery Grant (2017–2022), Connaught New Researcher Award (2017–2019), and Brain Tumour Foundation of Canada Grant (2021–2023). Over 50 publications in high-impact journals like J. Am. Chem. Soc. , ACS Med. Chem. Lett. , and Biochemistry . Labs & Teams: The Beharry Lab collaborates with institutions globally, focusing on translational research from bench to clinic. Current projects include enzyme-activatable prodrugs and theranostic agents for precision oncology.
Aldo H. Romero is the Eberly Family Distinguished Professor of Physics and Astronomy at West Virginia University, where he serves as Director of Research Computing . His research integrates computational materials science, artificial intelligence, and high-performance computing to advance understanding of condensed matter systems, including strongly correlated materials, 2D materials, and magnetic compounds. Education : Ph.D. in Physics and Chemistry from University of California, San Diego (1998) Awards : Eberly Family Distinguished Professorship Romero’s group specializes in Density Functional Theory (DFT) , Dynamical Mean Field Theory (DMFT) , and machine learning for materials discovery. They develop open-source tools like PyProcar , MechElastic , and ABINIT , focusing on properties such as electronic structure, elastic behavior, and magnetic interactions. Recent work explores chiral materials , AI-driven optimization , and high-throughput computational methods for material design. Key trends in publications include studies on chiral symmetry in kagome lattices, machine learning for material properties, symmetry-based structure prediction, and DFT+DMFT analysis of correlated systems. Collaborations span Germany, France, China, Spain, and Italy, with applications in energy storage, quantum technologies, and spintronics. Scientific contributions encompass: Development of Fireball and ABINIT codes Machine learning for Hubbard U parameter optimization Chiral material discovery via symmetry arguments Pioneering work on 2D Rashba systems Labs and teams include: AI WVU Discussion Group (founded by Romero) West Virginia University Computational Materials Group Collaborative efforts with international institutions Grants and proposals highlight initiatives like the NSF & NVIDIA Open Multimodal AI Infrastructure (OMAI) program and Spencer Foundation support for AI in education. The group works with over 20 graduate students and 4 postdoctoral researchers, emphasizing interdisciplinary training and global collaboration.
Daniel Abrams is Professor of Engineering Sciences and Applied Mathematics and (by courtesy) Physics and Astronomy at Northwestern University, where he co-directs the Northwestern Institute on Complex Systems (NICO). His research creates simplified mathematical models to study nonlinear dynamics across physics, social systems, and geoscience. Education includes: NSF Postdoctoral Research Fellow in Mathematical Sciences, MIT PhD Theoretical and Applied Mechanics, Cornell University BS (Hons) Applied Physics, Caltech Research explores coupled oscillators, social physics, and mathematical geoscience through analytical techniques. Current projects examine synchronization phenomena, opinion dynamics, and medical applications of dynamical systems. Recent publications show strong focus on mathematical modeling of social interactions and healthcare applications, particularly pain assessment in sickle cell disease using wearable technology and machine learning. Awards and honors: James S. McDonnell Foundation Scholar (2010-2015) NPR's 'Most Likely to Blow Your Mind' award (2012) Fulbright Scholar (2010) Multiple NSF fellowships Leads interdisciplinary teams at NICO developing data-driven approaches to complex systems. Maintains collaborations with medical researchers on technology-assisted pain management.
Derek Lomas is a researcher at Delft University of Technology’s Faculty of Industrial Design Engineering, specializing in Human-Centered Design and Human Technology Relations. He holds a PhD in Human-Computer Interaction from Carnegie Mellon University, an MFA in Social Design from UC San Diego, and a BA in Cognitive Science from Yale University. His research focuses on AI-driven wellbeing solutions, neurodesign, and educational technology. He leads projects like NeuroUX (mobile cognitive assessment software) and Zensus (patient wellness tracking), and has developed over 40 learning/assessment games. Key areas include music perception via EEG, AI alignment with human emotions, and design methodologies for positive AI. Education: B.A. Cognitive Science, Yale University, 2003 MFA Social Design, UC San Diego, 2009 PhD Human-Computer Interaction, Carnegie Mellon University, 2014 Research Interests: AI for wellbeing and mental health Neuroscience-driven design (EEG, brain-computer interfaces) AI ethics and human-AI interaction Game-based learning and assessment Key Projects: NeuroUX : Cognitive assessment software used in psychiatric research Zensus : AI-driven patient wellness monitoring system FactFlow : AI math fluency tool for children Vibe Research Labs : Exploring AI and positive human experiences Awards: Social Innovation Fellowship (PopTech) MacArthur Foundation Digital Media Learning Grant White House National STEM Game Competition McGinnis Business Plan Competition Winner Grants & Collaborations: Consultancy: Playpower Labs (UX, data science, software) Partnerships with UC San Diego, Brown University, and UT Austin
Prof. Dieter De Witte serves as a Professor at Ghent University, dedicating 50% of his time to the Internet Technology and Data Science Lab (IDLab) while simultaneously contributing 50% to the Royal Museums of Fine Arts Belgium (RMFAB) in Brussels through a FED-tWIN mandate from Belspo. At RMFAB, he spearheads the strategic overhaul of digital infrastructure toward FAIR-compliant and data-driven systems, while at Ghent University he collaborates with Prof. Steven Verstockt on applied AI projects across heritage, mental healthcare, and education domains. His academic foundation includes a Master's in Engineering Physics from Ghent University (2008) followed by doctoral research on Big Data technologies and FAIR data for life sciences. Prior to returning to academia in 2021, he gained industry experience as an AI consultant and team lead at Telenet and Ordina. De Witte's research centers on AI-driven transformation of cultural heritage through FAIR data publication , collection enrichment , and intuitive querying interfaces . His technical expertise spans multimodal algorithms, image segmentation, pose estimation, large language models (LLMs), and semantic technologies including SPARQL and IIIF. Current projects focus on human-in-the-loop AI systems that combine diverse AI building blocks for practical heritage applications. Analysis of his 15 most recent publications reveals a clear trajectory from early bioinformatics work (2007-2018) on genomic motif discovery and life sciences data infrastructure toward contemporary cultural heritage applications (2023-2024). Recent outputs demonstrate innovative fusion of pose estimation, linked data frameworks, and multimodal AI for museum contexts, highlighting increasing specialization in AI enrichment of digital collections while maintaining core expertise in FAIR data principles. His FED-tWIN grant enables critical knowledge transfer between academic research and cultural heritage institutions, supporting development of next-generation digital infrastructure at RMFAB. Current projects involve creating AI tools for intuitive collection exploration and systematic enrichment of heritage assets through advanced computational methods. De Witte operates within Ghent University's Internet Technology and Data Science Lab (IDLab), participating in interdisciplinary teams developing applied AI solutions. His work bridges technical innovation with practical implementation in cultural institutions, focusing on sustainable, interoperable systems that enhance public access to digital heritage collections through cutting-edge AI interaction paradigms.
Prof. Sander Koole is a Full Professor at the Faculty of Behavioural and Movement Sciences and APH - Mental Health at Vrije Universiteit Amsterdam. He specializes in Clinical Psychology and serves as Chief Editor at Taylor & Francis since 2018. His research focuses on Emotion Regulation , Interpersonal Synchrony , and Adaptive Behavioral Systems , with over 195 publications and 15 supervised PhD theses. His work bridges clinical practice with computational models, addressing topics like mental health in pandemics and human-robot interaction. Research Interests: Emotion Regulation Dynamics Multimodal Interpersonal Synchrony Computational Models of Coregulation Psychotherapy and Mental Health Cultural and Cognitive Foundations of Behavior Awards & Grants: Best Paper Awards (2015, 2000, 1995) Consolidator Grant (2011) Academic Excellence in Psychology (1991) Advising & Grants: Supervisor of 15 PhD theses. Research funded by grants addressing emotion regulation and mental health. Active in media commentary on topics like stress, self-esteem, and societal well-being. Labs/Teams: Collaborates on projects involving computational neuroscience, clinical psychology, and interdisciplinary teams at VU Amsterdam.
Martial Hebert is the Dean and University Professor of Robotics at Carnegie Mellon University's School of Computer Science (SCS), leading since August 2019. His career spans decades at CMU's Robotics Institute (RI), where he served as Director (2014-2019) and Professor (1999-present). Broad research interests in computer vision, perception for autonomous systems, and 3D environment modeling. Current PhD advisees include Zhipeng Bao, with numerous past PhD and Master's students listed. Editor-in-Chief of the International Journal of Computer Vision and member of IEEE Robotics and Automation Society. His research focuses on computer vision and robotics , emphasizing 3D data interpretation, object recognition, and machine learning applications. Recent articles highlight advancements in 3D vision , diffusion models , and disaster response robotics , reflecting a trajectory from foundational algorithms to applied autonomous systems. Notably, he pioneered the first master's program in computer vision in the U.S. Hebert's leadership in academic and research spheres includes directing the RI and securing an operating budget peak during his tenure. His work bridges perception, intelligence, and autonomous systems, with applications in disaster scenarios , LiDAR point cloud detection , and video forecasting .
Josiah Wang is a Senior Teaching Fellow in the Department of Computing at Imperial College London, where he also serves as the degree coordinator and admissions tutor for the MSc Computing program. He teaches Python Programming and Introductory Machine Learning courses. Previously, he held postdoctoral research positions at Imperial College London and the University of Sheffield, focusing on Artificial Intelligence with specializations in Computer Vision and Natural Language Processing. He earned his PhD in Computer Science from the University of Leeds (2013), advised by Katja Markert and Mark Everingham. His research explored cross-modal learning, including pioneering work on learning object recognition from textual descriptions and multimodal machine translation. Notable contributions include the MultiSubs dataset and methods for phrase localization without paired training examples. Education: PhD in Computer Science, University of Leeds (2013) MSc in Computing, University of Leeds (2007), supervised by David Hogg Research Interests: Josiah's work bridges AI and human cognition, emphasizing systems that learn from limited data and integrate multiple modalities. His contributions span visually descriptive language annotation, image captioning, and multimodal translation. Though no longer active in active research, his legacy includes foundational papers on object recognition from text (BMVC 2009) and unsupervised phrase localization (ICCV 2019). Grants & Projects: MultiMT and MMVC projects (Imperial College London) CHIST-ERA ViSen project (University of Sheffield) Labs & Teams: Collaborated extensively with interdisciplinary teams at Imperial, Sheffield, Lyon, and Barcelona, including work on multimodal machine translation and visual grounding systems.
Mike Mamalakis is a Senior Research Associate at the University of Cambridge, affiliated with the School of Clinical Medicine and Department of Computer Science and Technology . His research focuses on applied and theoretical AI, particularly in Deep Learning , Multi-Modal AI , and eXplainable AI for biomedical and healthcare applications. Research Themes : Medical imaging, neuroscience, brain tumors, Alzheimer's disease, and foundation models. Collaborations : University of Sheffield (Richard Clayton, Andriew Shift, George Panoutsos), University of Cambridge (Pietro Lio, Murray Graham, John Suckling), and Cancer Research UK Cambridge Centre. His recent work involves developing predictive models for neuroscience using multimodal data (text, imaging, phenotyping, genomics) and applying gradient-based explainability methods to study brain tumors. Publications highlight AI-driven biomarker discovery in COVID-19 , pulmonary hypertension , and cardiac arrhythmias . Current affiliations include the School of Clinical Medicine and CRUK Cambridge Centre. Contact : mm2703@cam.ac.uk | Room GC01, William Gates Building, University of Cambridge.
Robert Clarke is a Professor of Breast Biology and Director of the Lobular Moon Shot Project at the Manchester Breast Centre, based at the Oglesby Cancer Research Building, University of Manchester. He leads research focused on breast cancer stem cells, metastasis, and prevention strategies. His work contributes to understanding cancer initiation and developing targeted therapies. Clarke holds a BSc (Hons) in Biology with European Studies (University of Sussex and Université Grenoble Alpes) and a PhD in Molecular Oncology from the University of Manchester (1995). His career includes postdoctoral training at The Christie NHS Foundation Trust and roles as a Cancer Research UK Research Fellow, Lecturer, and Reader before becoming a Professor. Research Interests: His lab investigates breast cancer stem cell regulation, hormonal influences on tumor development, and metastatic microenvironment modeling. Key themes include identifying self-renewal pathways for therapy and prevention strategies. He also explores circadian clock influences on cancer progression and chemoresistance mechanisms. Grants & Projects: Leading projects like Developing Targeted Strategies for Precision Breast Cancer Prevention and collaborating in initiatives such as the EurOPDX Consortium. He contributes to the NIHR Manchester Biomedical Research Centre and Cancer Manchester Research Centre . Teaching & Mentorship: Teaches Cancer Stem Cells to final-year Biology and Oncology students. Supervises PhD and postgraduate researchers, including those funded by Breast Cancer Now and MRC grants. His group includes over 15 researchers focused on breast cancer biology and therapy. Professional Engagement: Member of the Medical Research Council Clinical Training Panel, Swiss National Science Foundation, and editorial boards of Breast Cancer Research and Journal of Mammary Gland Biology and Neoplasia .
Dr. Noura Vyas is an Associate Professor of Mental Health in the Department of Psychology at Kingston University's Faculty of Business and Social Sciences. She serves as the Academic School Lead for Civic Engagement and has been with Kingston University since 2012, having previously held a Senior Lecturer position at Middlesex University. Dr. Vyas is also an Honorary Senior Lecturer at Imperial College London, Imperial College Healthcare NHS Trust. Dr. Vyas completed her PhD in Psychiatry at the Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London in 2008. Her educational background includes a BSc (Hons) in Psychology from City University London. She is a Chartered Psychologist and Associate Fellow of the British Psychological Society, a Chartered Scientist of The Science Council, and holds a Senior Fellowship with the Higher Education Academy. Dr. Vyas's research program focuses on understanding the pathophysiology of schizophrenia, particularly early-onset schizophrenia (EOS), using multimodal approaches including clinical assessment, cognitive testing, and advanced neuroimaging techniques. Her work investigates neurocognitive functioning in EOS patients and their first-degree relatives, brain oscillations and structural/functional abnormalities using magnetoencephalography (MEG), diffusion tensor imaging (DTI), and positron emission tomography (PET), and the effectiveness of mindfulness interventions on wellbeing in typical children. Her research bridges neuroscience, genetics, and clinical psychiatry to uncover the complex mechanisms underlying psychotic disorders. Analysis of Dr. Vyas's publications reveals a consistent trajectory from basic neuroimaging and genetic studies of schizophrenia toward more integrated approaches examining the interplay between multiple biological systems and clinical manifestations. Her work spans psychiatry, neuroscience, genetics, and psychology with increasing emphasis on translational research that connects basic findings to clinical applications. 2017: Women of the Year Award, British Asian Achievers Award 2017: Team Excellence Award, Succeed Canvas project, Rose Awards 2017: "Highly Commended" STEM Leader, Forward Ladies National Awards 2017: 'Inspirational Role Model of the Year' (Finalist), European Diversity Awards 2017: 'Women of the Future Awards – Science' (Finalist) 2017: Marquis Who's Who Lifetime Award 2017: Young Investigator Award, 13th World Congress of Biological Psychiatry 2016: Outstanding Women in Science, Technology & Mathematics (STEM), Precious Award 2016: Winston Churchill Travelling Fellowship 2011: Lindemann Trust Fellowship, English-Speaking Union Dr. Vyas has secured significant research funding from diverse sources including Fulbright, Winston Churchill Memorial Trust, UKRI, and institutional grants. Her leadership roles include Faculty Champion for Canvas implementation (2016-2018), KAPS Panel Assessor (2018-present), and Course Director for the Foundation Year in Social Sciences (2018-2022). She currently co-leads the MSc conversion (online) degree program and teaches across undergraduate and postgraduate courses including Psychology MSc, Clinical Applications of Psychology MSc, and various BSc Psychology programs. Dr. Vyas is actively engaged in public mental health initiatives as a series guest speaker on mental health with Resourceful Women's Network, Riverside Radio, Healing our Earth online platform, and Dharma Mandir. She organizes public engagement talks supporting mental health initiatives and contributes to KU Blogs on mental wellbeing topics. Her Instagram account @mentalhealth_connect serves as a platform for public education on mental health issues.
Dr. Shinichi Nakajima is a Senior Research Lead at the Technical University of Berlin, affiliated with the BIFOLD (Berlin Institute for the Foundations of Learning and Data) and the AIP – RIKEN Center of Advanced Intelligence Project . He leads the research group “Probabilistic Modeling and Inference” at BIFOLD. His academic journey includes a Master’s in Physics from Kobe University (1995) and a PhD in Computer Science from Tokyo Institute of Technology (2006). Prior to academia, he worked at Nikon Corporation (1995–2014) on statistical analysis, image processing, and machine learning. His research focuses on Bayesian inference , generative modeling , explainable AI , and quantum computing , with applications in computer vision, natural language processing, and scientific computing. Notable projects include developing NeuLat (a neural sampling toolbox for lattice field theories) and advancing techniques for symbolic XAI to enhance AI transparency. Dr. Nakajima has published extensively on topics such as diffusion models, federated learning, and physics-informed neural networks. His work bridges theoretical foundations (e.g., Bayesian learning) with practical applications in quantum computing and biomedical imaging. He actively contributes to open-source tools and collaborates with industry and academic institutions globally. Key technical achievements include improving sampling efficiency in quantum eigensolvers, enhancing brain source reconstruction via 3D neural networks, and developing anomaly detection systems using self-supervised autoencoders. His research emphasizes computational efficiency and robustness against adversarial attacks, leveraging Langevin dynamics and gradient-based optimization methods.
